{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 散点图与直方图",
   "id": "e96495de76f4f424"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T06:11:02.125604Z",
     "start_time": "2025-09-08T06:11:00.349102Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 黑体、微软雅黑\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "path = 'D:/2506A/monty03/day10/file/'"
   ],
   "id": "785d8a0191af4612",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 1. 散点图\n",
    "- plt.scatter(x, y, s=None, c=None, marker=None, alpha=None, edgecolors=None, linewidths=None, label=None)\n",
    "- 参数解释\n",
    "    - x,y 横纵坐标\n",
    "    - s 点的大小\n",
    "    - c 点的颜色\n",
    "    - marker 点的形状\n",
    "    - alpha : 透明度\n",
    "    - edgecolors: 点的边缘颜色\n",
    "    - linewidths : 点的边缘宽度"
   ],
   "id": "42b63534685ddeb2"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-05T08:22:45.294408Z",
     "start_time": "2025-09-05T08:22:44.891233Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 读取Excel文件\n",
    "data = pd.read_excel(path + '散点图.xlsx')\n",
    "\"\"\"\n",
    "散点图\n",
    "\"\"\"\n",
    "# 显示所有的列\n",
    "# pd.options.display.max_columns = None\n",
    "# 显示所有的行\n",
    "# pd.options.display.max_rows = None\n",
    "\n",
    "# 删除包含所有缺失值的列\n",
    "data = data.dropna(how='all', axis=1)\n",
    "\n",
    "# # 删除包含任何缺失值的行\n",
    "# data = data.dropna(axis=0)\n",
    "\n",
    "# 散点图\n",
    "plt.scatter(data['身高'],data['体重'],s=data['身高'],c=data['身高'],marker='o',alpha=0.5)\n",
    "\n",
    "plt.colorbar()\n",
    "\n",
    "# 设置横纵坐标标签\n",
    "plt.xlabel('身高')\n",
    "plt.ylabel('体重')\n",
    "\n",
    "plt.xlim([0,250]) # 设置横坐标的值\n",
    "\n",
    "plt.title('身高体重散点图')\n",
    "\n",
    "\n",
    "print(data)"
   ],
   "id": "eca74871ac7a00f7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    姓名 性别   身高   体重\n",
      "0  聂茹凤  女   30   36\n",
      "1  韩耀祖  男   50   44\n",
      "2  谭鑫宇  女  188   60\n",
      "3  崔龙腾  男   45   29\n",
      "4  刘千琪  女  150   70\n",
      "5  李兆康  男  140   80\n",
      "6  李欣桐  男  170   90\n",
      "7  李欣桐  女  120  100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhsAAAHDCAYAAACNothiAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAT5tJREFUeJzt3Xl8VPW9P/7XmT3bZDIkJBAGSAJCESISIzF4cUOkECr4c2GpFnpV1NblB+ZRUvHaW7kkvQVMrSKNYmOoFLeISi+KBYoUDSUIaAwiBKIJaxKSmUkms5/vH5EpQxYzmZmcmczr2cfn8eicbd75PAbP+3y2I4iiKIKIiIgoSGRSB0BEREQDG5MNIiIiCiomG0RERBRUTDaIiIgoqJhsEBERUVAx2SAiIqKgYrJBREREQcVkg4iIiIKKyQYREREFFZMNIh8dPnwY9957Lz799NM+nd/a2opVq1Zhx44dvT7H4XDg2LFjnbY3NDRg8+bNcLlcfYrlcqdPn+52X3NzM0wmEwCgpaUFLS0tXvudTqfP3/evf/0L//jHP3w+j4jCi8Dlyol889JLLyE/Px+1tbVITEzs9jiXywW73Q6NRgNBEDzbRVHEsGHDkJubi7feesvrHFEUYbVaoVKpIJfLPdv37t2L66+/Hnv27MH111/v2b5v3z7k5OTA6XR6HX+5b775BhcuXIDFYoHFYsGFCxfQ1NSExsZG1NfX48SJE/jqq6/Q2tqKvXv3Ijs7u9M18vLyMGrUKBQXF2PevHlQKBT4y1/+4tmfnZ2NKVOmoLi4uMf6u9Q999yD+vp67N279weP/fnPf44///nPvb42AGRlZaGystKnc4go8BRSB0AUDurq6jB8+HAolUrIZB0NgsOGDfM6xmazYdu2bZgxYwYAYM+ePbjpppu6vebbb7/tlYRcav/+/bjmmms8nz/88ENcccUVmDJlitdxF+PpKdEAgDfeeAMbN27EoEGDkJCQAJ1OhyNHjuD8+fN4/PHHccsttyA5ORkpKSkwGAxe5zocDgiCAI1GA7VaDQBQqVRQKBRob29HVFQUmpqa8Pnnn2PFihU9xnE5jUYDh8OBr7/+utN3tre345prrvHUt0KhwNy5c7Fp0ybPcdu2bYPb7casWbM6XbuoqAgff/yxT/EQUXAw2SDqhYs32e3bt+PGG2/s8hhBEKBSqTyfc3Nzce7cOURFRcHpdP5gV4dSqYQgCLDb7YiPj/dsd7lcKC0txUMPPYS2tja43W7P9zidTgiCAKvV6jne7XbD7XYjNjYWTqcTx44dw8KFC3Hvvfd6fd+aNWvwz3/+E3fffbfXdpPJhKamJmi1WiQnJ+O1117DkiVLPH/jH/7wBzgcDgBAaWkpTCYT3nrrLcTFxeGmm27yxHKxVefSOumqzg4ePOjVWnPx3Pb2djQ3NyM2NhZAR7Ihk8mg0WgAdCR3+fn5SE5Oxo9//GNPUiKTyTzJ0A8lYUTUP5hsEPXCxRtme3s7Wltbf/C4i/9/8ODBAIC5c+fio48+glar7fK8CxcuoKioCEuXLu207/3330d9fT1yc3Px5JNP4k9/+lOnY6Kiorw+33777diyZQuampowefJkaDQaz834otbWVtjtduTk5HhtF0URDocDTzzxBP7rv/4L8+fPx6JFi/DTn/4UI0eORFFRERYtWgSFQoEXXngBarUar776KtLT0/HII4/gxIkTOHr0KKZNm4alS5di8uTJ3dYXAEyZMqVX4zaUSqXX59/+9reoqalBTU0NYmJiPNsLCwuxfPlyAOj0NxORNJhsEPlg5syZfTovKioK9913H9avX9/l/uuvv97zxH4pURSxatUqAB2tAGvXrsWaNWugVqvx5JNP4oMPPsC3336LoUOHoqysDNdffz3cbrdnsGZycrJnUOflVqxYgb///e+oqKjoMfZbbrkF+/bt87QSrF69GkBHC8qGDRtQWlqK/fv345VXXsF//ud/Yt26dXjnnXfwxhtvdLrW+fPnYbfbPZ8tFgtsNhvq6+u9jtNoNJ3Gw1za5fT++++jqKgIV155JT799FNotVosXboUn3zyCZYtW9bj30NE/Y/JBpEPdu3a1WU3SmNjI5KSkgAA1dXViIuL8xr74HA4UFJSgldeeaXL67pcLtxxxx2dtv/lL3/xenKPjo4G0JGElJeXY9asWXj55ZeRl5eHzZs3e2Lrquti1KhRqKmp6bT98nEjH330EaZPn+75/Mknn3TbFWI0GpGTkwONRoNTp04B6Ji1crEuLjdz5kwcOHCg0/bLx4ncdttt+PDDD7u8RktLCx544AH8/ve/x/79+3HzzTcjLy8P77zzDv75z392agEhIukx2SDywf333+8ZQ3Cpi+MxduzYgXXr1uHGG29EWVmZZ6zH5bNOesPpdOI3v/kN/ud//gdPP/20177XXnsNLS0tuOuuu/Dyyy/j4YcfxuTJk7Fs2TKMHj26y+tpNBqsWbMGDz74IADg2Wefxa5du7Bz507PMXFxcZ1aWFQqFd544w08/PDDnmQHANra2rB8+XLcddddcLvdqKqqAgCcOHGi22RDo9HgZz/7GUpLS7v9uxctWoQLFy50u1+n0+Hw4cNISUnBJ598ghkzZuDAgQNYvnw59Hp9t+cRkXSYbBD14OJgR7fbDQB45ZVXumzZ+OKLL3DVVVfh5ZdfxquvvuqZHXHTTTfhs88+8+k7R40ahaqqKigUCmzbtg2jR4/2SjbOnTuHX//613jsscc8N9cJEybgrrvuwuLFi/Hxxx93GsMBAHK5HMuWLevUzRAXF+f1uatxDoIgQK/X4/jx455tiYmJiIuLw69+9Sts2bIFr776KkRRxKFDhzB37twu/7aeBoteqqfWiWPHjqG8vBxbt27FV199hd/85jcYO3YsVqxYgXXr1uHWW2/1zAgiotDA0VNEPVi0aBGioqIwaNAgAB3JgyAIncpVV10FAHj99dc7TcN88sknYbVae1VWr17t1a1xxRVXeH02m82YOXMmkpOTO00zff7559HQ0ICf/OQnaGpq6vS3uFwuPPvss6irq0NdXR0ee+wxXH311Z7PX331FYB/J1aXUqlUqKmpgUKh8JSmpiZP8pCbm4uzZ89i48aNOHToEK6++uou69Nms8FisaC+vr7bYrFYelwgTBAEbNmyBXfddRc+//xzPP/88ygvL8e6devw1ltvISoqytOiREShgS0bRD149tlnsXz5cigUnf+pnDlzBvn5+aipqcH69evx05/+tNNUy0tbCT777LNun/j/9Kc/4fbbbweAbqdrfvnll1iyZAnMZjO2b98OwDsxiImJwbvvvotZs2Zh/PjxOHDgAIYOHerZb7Va8fTTT3fqkrl8vMSl02gvlZGR0all46LBgwdjypQpuP/++6FUKpGbm9vlNWw2G956660f7Fa67bbbut03atQoT2uRy+XCG2+8geeeew633HILjh49io0bNwIAVq5c2eN3EFH/YcsGUQ9Gjx6NiRMnYvz48Z4yevRo7Nq1C3fddReUSiUOHDiAhQsXdnn+pa0SsbGxUCgUOHv2rFfRaDTozUK+KpUKI0aMwN69e/Hkk08iKioKWVlZcLlcEAQBSqUSn3zyCSoqKrBu3TqvRMPlcuH48eMQRdFTnnrqKUyePNlrmyiKuOWWW3oVz+V++ctfwuFwYP78+UhISOi0/+TJk/joo486fd/F4nK5PP9/69atPxjDyZMnMXjwYKxfvx733nsvampqMHLkSJ/jJqLgY7JB1EunTp3CypUrkZGRgTVr1qCwsBAVFRW44ooruj3n0htmTzfP7lYSvdSYMWOwfft2pKWlYdOmTTh//jw++eQTyGQymEwmNDc342c/+xmSk5M7taDce++9iImJQWJiIlJSUpCSkoLi4mJ8/vnnns8pKSmIj4+HRqPBv/71r05/x4kTJ6DRaDzl8q6aL774AgBw8ODBLme9PProo7jhhhsAABs3bsSjjz7q2XfHHXfgD3/4A4COxOi6667DSy+95HX+5YuiDRkyBK+//jqUSiXuvfdeTJgwwdMVdFFXXUJE1P+YbBD14NNPP0VRURGuv/56DB8+HFu3bsWaNWtQU1ODhx9+GDabDceOHfO82+PyLpBLb5Ctra2wWq1ITEz0Kq2trZ6boiiKXXbZXFwV9CK9Xo+kpCRoNBrPaqE6na7LgaHnzp3DCy+8gHPnzqG2thbHjx/H8ePH8Ytf/AITJ070fD5+/DhOnTqFpqYmpKeney1eZrfbkZ6eDqvVCovFgj179iAmJgZyuRytra148MEH8dprr+Ef//gHRo8ejUmTJmHlypUwGo0AgJqaGmzbtg35+fkAOlYDXb9+vWe67Pjx4/Hss8/iwoULkMvluPnmm1FQUOD1Yjin04n29nbP2I7GxkaMHz8ev/3tb7F//37813/9F+Lj4z37TSZTn14OR0RBIBJRt1544QUxKSlJfPTRR8VDhw512t/e3i4OGTJEBCAOHjxYbGlp8dp/3XXXiU899VSvvqugoECcNGmSWFxc3GnfkCFDxO3bt3favmfPHhGA2N7e3u11tVqtqFarRa1WK8bHx/9g0Wq1okajEVevXu25RllZmZiRkeH5PHXqVHHChAnil19+KU6cOFG87777xPPnz3v2b9y4UczKyhI///xzURRF8e677xbT09NFh8MhiqIoWq1WUafTiU8//bQoiqLY0tIixsfHiytWrBBFURRbW1vFlJQU8e677/Zc84EHHhAB+FSys7N7VfdEFFx86ytRD0RRhNPp7HEq5rZt2yCTyXDdddd1uxx5b1gsFkRFRfWqS0VqLpfL04pjt9t/cErrjh07YLPZvFZg3bJlC3Jzcz1Luv/f//0frr32Ws/A0/LycgwdOtSznPrPf/5zmEwmvP32272KceXKlSgvL8fnn3/u899HRIHFZIOIwkJjYyNEUex2wTAiCl1MNoiIiCioOECUiIiIgorJBhEREQVVRKwg6na7cfr0acTFxYXF4DsiIpKOKIowm80YOnRol+8KChSr1Qq73e73dVQqVacXKIaaiEg2Tp8+3WlJZiIiop7U1dVh2LBhQbm21WpF2shknD1n8vtaKSkpOHnyZEgnHBGRbFx8q2VdXZ1fUxOJiGjgM5lMMBgMnd6IHEh2ux1nz5nw7ZH/hjau70mCyWzFiB89A7vdzmRDahe7TrRaLZMNIiLqlf7odo+LUyNO2/e3FIsIjwmlEZFsEBERhSLx+//5c3444GwUIiIiCiq2bBAREUnk4ot8/Dk/HDDZICIikkikdKMw2SAiIpKIWxTh9uOtIf6c25+YbBANQE63Gw3trbC6nFAIMmhVGsSrQ3daHBENbEw2iAYQo82KL5rOoOLsdzhjMcPpdkMQBEQrlLgqcQiyklKRrtVDHsRVEYmo9zhmg4jCysGG03j3RBXOW1qhUSgxSBMNpUwOURTR6rDjH6dO4LOz3+LqxFTcOWo8YpV9n9tPRIHBMRtEFDYqz9dj87HDcLrdGKVLhFzwbrnQKJRIjIqB2W7Dp2e/RbvLgZ+NmYRopUqiiIkokrAtlSjM1bUa8U5NFVxuESPiEjolGpeKU6mRFqfHoYbT2Fr7dT9GSURdEQNQwoFkyUZTUxPS0tJQW1vr2VZVVYXs7GwkJCQgPz8f4iWjbHvaRxTJKs/V44LVAkNsfK+O1ygUSIqKxYGGU2hobwtydETUEzdEv0s4kCTZaGxsRF5enleiYbPZMHv2bGRlZaGyshLV1dUoLS39wX1Ekcxot+JAQz0S1NE+vcchQR2FZls7vmw6G8ToiIg6SJJszJs3D/PmzfPatm3bNhiNRqxduxYZGRlYtWoVNmzY8IP7iCLZcWMTmqwWJGqifTpPJgiIUahw4Hx9kCIjot6Qohulq56FjRs3Yvjw4YiNjcW0adN63evQW5IkGyUlJXj88ce9th0+fBg5OTmIju74j2ZmZiaqq6t/cF9XbDYbTCaTVyEaiNodDgBCn6ayahQKmBy2sFkUiGgg6u9ko6uehZqaGjz11FPYsmULqqurMWLECCxatAhA4HoWJEk20tPTO20zmUxIS0vzfBYEAXK5HM3NzT3u60phYSHi4+M9xWAwBP6PICIiCjNd9SwcPHgQOTk5mDRpEoYPH47Fixfjm2++ARC4noWQmY2iUCigVnvP+9doNLBYLD3u60pBQQGMRqOn1NXVBS1uIilFKZUARLjcbp/PtTqd0CrVkPkw1oOIAsst+l8AdGrNt9lsXX5fVz0L48aNw86dO3Hw4EEYjUa8+OKLuPXWWwH43rPQnZBJNvR6PRoaGry2mc1mqFSqHvd1Ra1WQ6vVehWigWhU/CAM0kSj0dp14t0dtyiizWlH1uBhQYqMiHojUN0oBoPBq0W/sLCwy+/rqmdh3LhxuPPOOzFp0iTodDrs27cPq1evBtBzr4MvQibZyM7ORkVFhedzbW0tbDYb9Hp9j/uIIlm8SoOspGFotll8GrTVbGtHgjoKEwalBDE6IuqNQIzXqKur82rRLygo6PX3V1RU4IMPPsC+fftgNpsxf/58zJw5E6Io+tyz0J2QSTamTp0Ko9GIsrIyAEBRURGmTZsGuVze4z6iSHdN8jDoNdGoazX26nir04mG9lZkJaUiKSomyNERUX+4vDX/8gShJ2+88QbmzZuHa6+9FrGxsVi5ciVOnDiBw4cP+9yz0J2QWa5coVCgpKQECxYsQH5+PlwuF3bv3v2D+4ginSE2HndmjMdfjx3Gt+ZmDIuN73YVUbPdhvo2I65OGoq8kWP7OVIiulwovIjN6XR6dYuYzWa0tbXB5XIhOzsbr7zyimdfX3sWJE02Lm/2nTNnDo4dO4bKykrk5uYiKSmpV/uIIl3W4GGQCTK8e6IKx1savV7E5v7+RWwXbBao5XJMGTIC/1/GeL4XhSgEuCHAjb4P0vbn3IumTJmCn//853juueeQnJyMV155BcnJycjMzIQgCJ6ehfvuu6/PPQsh07JxUWpqKlJTU33eRxTprk4ainStHl80ncG+c3U43WaC0+2GTBAQpVDiptQMZCUNRRpfMU9El7jnnntw9OhRFBcX48yZMxg/fjzKy8uhVCoBICA9C4IYAS8ZMZlMiI+Ph9Fo5MwUighOtxsN7a2wupxQCDJoVRrEqzVSh0UUFvrjnnHxO459txJx2r7/2zSbrBg9fEXQ72+nTp3yq2ch5Fo2iMh/CpkMQ2KYWBOFOlEUIIp97wrx51xf+NuzwLZUIiIiCiq2bBAREUnE/X3x5/xwwGSDiIhIIiIEiH7MKPHn3P7EbhQiIiIKKrZsEBERSSRSWjaYbBAREUmEYzaIiIgoqMJl6qu/OGaDiIiIgootG0RERBLhmA0iIiIKqo4xG/68iC08sBuFiIiIgootG0RERBJhNwoREREFlfh98ef8cMBuFCIiIgoqtmwQERFJRITg1wBRdqMQERFRj7ioFxEREVEAsGWDiIhIIpyNQkREREHl9nPMhj/n9icmG0RERBKJlJYNjtkgIiKioGLLBhERkURE+DkbJUxaNphsEBERSSRSxmywG4WIiIiCii0bREREEomUd6Mw2SAiIpIIZ6MQERERBQBbNoiILuFyW2FxnoZLtAIQIRfU0MgHQynXSh0aDUBuUYDbj9ko/pzbn5hsEBEBsDrPo9lahSbrAdhcDXCLDkAABCigksUjQXMV9JqrEK0wQBDC4z/wFPoipRuFyQYRRTS36MDp1u1obP8X7G4jlLI4RCmGQAYVAECEE3ZXC8627UCD5TPo1ONg0N4OpSxO4siJwgeTDSKKWC7Rju9M5Whs3we1XA+tcnSnVgsBSmgUSVCLiXCKrWiyVsIhtiJNOx8qebxEkdNAESktGxwgSkQRSRTdONW6DY3t+xCtGAq1fFCP3SOCIEApi0OsMg0m29f41vwOnO72foyYBqKLi3r5U8IBkw0iikitjpNotHwGjXwwFLKYXp8nE5SIUYxAi60KzdZDwQuQIoMIiH6UcFlog8kGEUWkC9ZDcIn2PnWFyGVqyKFCk7USbtEZhOiIBpaQSzb+/Oc/Y/z48dDpdJg/fz4aGxsBAFVVVcjOzkZCQgLy8/MhimGSzhFRyLG5LqDFVgW1XN/na2jkSWhz1KHVfiKAkVGkuThmw58SDkIq2fj73/+Oxx57DM899xwOHz4Mk8mEuXPnwmazYfbs2cjKykJlZSWqq6tRWloqdbhEFKbM9uOwu1qgkun6fA25LApu0QGj/WjgAqOII0Wy0dTUhLS0NNTW1nbat3z5csyePdtrWyAe9kMq2SgrK8P999+PW2+9FSNGjMDvf/97/POf/8TWrVthNBqxdu1aZGRkYNWqVdiwYUO317HZbDCZTF6FiOgip9sCATIIgn//CZQJSjjc/O8LhY/Gxkbk5eV1mWhUVVVh3bp1KC4u9mwL1MN+SCUbjY2NGD58uOezXC4H0FEBOTk5iI6OBgBkZmaiurq62+sUFhYiPj7eUwwGQ3ADJ6KwIsIFUfC/K1aADG7RHoCIKFL192yUefPmYd68eZ22i6KIJUuW4IknnkBGRoZn+7Zt23x62O9OSCUbEydOxPvvv+9povnzn/+Ma6+9FiaTCWlpaZ7jBEGAXC5Hc3Nzl9cpKCiA0Wj0lLq6un6Jn4jCg0xQBaSnWxRdkAvRAbgSRSpRFPwuADq15ttsti6/r6SkBI8//nin7S+//DIOHTqEtLQ0bN26FQ6HAwBw+PBhnx72uxNSycaTTz4Ju92OrKws5Obm4ne/+x1++ctfQqFQQK1Wex2r0WhgsVi6vI5arYZWq/UqREQXqWUJgJ+tEqIowg0HNPKkwAVG1EcGg8GrRb+wsLDL49LT0ztta21txYoVKzB69GjU19dj7dq1mDp1KqxWq88P+90JqRVE9Xo99u7di+PHj2P16tVobm7GggULsHr1alRVVXkdazaboVKpJIqUiMJZnGo0ohQpsLoaEa0Y2qdrONxGKGVa6DTjAhwdRRJ/l8q4eG5dXZ3Xg/XlD+g9KS8vR1tbG3bu3Am9Xo+CggJMmDABZWVlPT7sJyQk9Po7Qqpl46KhQ4eivLwchYWFkMvlyM7ORkVFhWd/bW0tbDYb9Pq+T1sjosgll6kxSHMNnO5WiKK7T9ewuZqgVY+BRp4c4OgoknQkG/7MRulweWu+L8lGfX09Jk+e7LmnKhQKZGZm4uTJk9Dr9WhoaPA6vi8P+yGZbPzxj3/E2LFjMWfOHADA1KlTYTQaUVZWBgAoKirCtGnTPANIiYh8laCeALUiCRbnKZ/PtbmaIBNUGKTJ4htgKewZDAa0t3svvf/tt99ixIgRAXvYD7lko6WlBf/7v/+LNWvWeLYpFAqUlJTgoYceQnJyMt5++20UFRVJGCURhTu1YhAMsbMhCHJYHKd6vXaAzXUBdrcRQ2KmQasaE+QoaaBzi4LfxV+zZs3CkSNHsH79etTX1+P555/HoUOHMGPGjIA97IfUmA0A0Ol0aGpq6rR9zpw5OHbsGCorK5Gbm4ukJA7KIiL/JGgy4RYdqDO/j1ZnDTSywVDI4rpsrXC622F1nQcADI2ZjpSYG9mqQX4Lhbe+6vV6fPjhh1i2bBmWLl2KlJQUbN68GSNHjgTQMYNlwYIFyM/Ph8vlwu7du33+jpBLNnqSmpqK1NRUqcMgogFkUFQWVPJ4nLP8E2b7MbQ7zkIpi4NM6OiTdotOONxmyAQlYpUjkBR9HRLUE5loUEBIlWxc3pKXk5ODvXv3dnlsIB72wyrZICIKhjjVKMQqM9DuPIVm6xdosVXDJVoBiFDIoqHTTIBecxXilOkQBI4Vo8jj78M+kw0iInSsHxCtHIZo5TCkxs2EW+xY1EiAgq0YFDSeV8X7cX44YLJBRNQFmaCUOgSKAH1Zcvzy88NByM1GISIiooGFLRtEREQSCYXZKP2ByQYREZFEIiXZYDcKERERBRVbNoiIiCTC2ShEREQUZP51oyBMulGYbBAREUlEFAWIfrzfxJ9z+xPHbBAREVFQsWWDiIhIIu7viz/nhwMmG0RERBLh1FciIiKiAGDLBhERkUQiZYAokw0iIiKJiN8Xf84PB+xGISIioqBiywYREZFEImWAKJMNIiIiiUTKmA12oxAREVFQsWWDiIhIIpEyQJTJBhERkUQ4ZoOIiIiCKlJeMc8xG0RERBRUbNkgIiKSCLtRiIiIKKg49ZWIiIgoANiyQUREJBFOfSUiIqKg4pgNIqI+srnsMDvb4XS7oJDJEaeIglqukjosIpIIkw0iCghRFHHe1oyvTd/hS+NJtDra4RbdkAkyxCqjkBmfjrHa4UhS6yAI4fE0RhRskbLOBpMNIvJbu8uGHec+R7WxFm1OK+IUUYhXxkAmCHCLbrQ6rfj7uQOoaKrGhPh03Jx8NVs6iMBuFCKiXml32vD+qb34ylSLJHU8BnfRchGt0CBJjIfJaUFFUzVane2YnZoLDRMOoojAZIOI+swlurH97H58ZarFsKgkqOXKbo8VBAHxyhioZUpUGU9AI1dj1tDJkAmcgU+RK1K6UfivnIj6rM5yHlXGk0jR6HtMNC6lkaswWJ2AKuMJnGpvDHKERKFO8HSl9KUgTLpRQi7Z2LhxI4YPH47Y2FhMmzYNtbW1AICqqipkZ2cjISEB+fn5EMMlnSMawL4y1sIhOhGj0Ph0XqwyClaXHUeM3wYpMqLwIAaghIOQSjZqamrw1FNPYcuWLaiursaIESOwaNEi2Gw2zJ49G1lZWaisrER1dTVKS0ulDpcoorXYW/G16TvolLF9Ol+njMFXplqYHZYAR0ZEPWlqakJaWprnYf5yM2bM8LrHBuJhP6SSjYMHDyInJweTJk3C8OHDsXjxYnzzzTfYtm0bjEYj1q5di4yMDKxatQobNmyQOlyiiNZkN6HV2Q6tMqZP52uVMWh1tOOC3RzgyIjCR8eYDcGP4tv3NTY2Ii8vr9tE4/XXX8dHH33k+Ryoh/2QSjbGjRuHnTt34uDBgzAajXjxxRdx66234vDhw8jJyUF0dDQAIDMzE9XV1d1ex2azwWQyeRUiCiyH2wlRFCHrY5+xXJDBJbphdzsCHBlR+AhUN8rl9zybzdbl982bNw/z5s3rct+FCxewbNkyjBkzxrMtUA/7IZds3HnnnZg0aRJ0Oh327duH1atXw2QyIS0tzXOcIAiQy+Vobm7u8jqFhYWIj4/3FIPB0F9/AlHEUAhyCELf+4zdECETBCgEeUDjIopEBoPB675XWFjY5XElJSV4/PHHu9y3bNkyzJ07Fzk5OZ5tvj7sdyekko2Kigp88MEH2LdvH8xmM+bPn4+ZM2dCoVBArVZ7HavRaGCxdN3XW1BQAKPR6Cl1dXX9ET5RRIlVREEtU6Hd1fUT1A+xOK3QyFWIVUQFODKi8OFfF8q/X09fV1fndd8rKCjo8vvS09O73L5r1y7s2LEDv/vd77y2+/qw352QSjbeeOMNzJs3D9deey1iY2OxcuVKnDhxAnq9Hg0NDV7Hms1mqFRdLwikVquh1Wq9ChEFVrImASOik9Fk61s35QW7GWkxKUhUxwc4MqLwEahulMvveZc/oPfEarViyZIleOmllzrdL3192O9OSCUbTqcT586d83w2m81oa2uDQqFARUWFZ3ttbS1sNhv0er0UYRIROp5wJujS4YYbDrfTp3Pt3x8/QZfO96QQSezZZ59FdnY2Zs2a1Wmfrw/73QmpZGPKlCkoLy/Hc889h02bNmHOnDlITk7GY489BqPRiLKyMgBAUVERpk2bBrmcfb1EUhoVl4phUYNRb2mEu5fD4t2iG6csDTBED0Z6zNAgR0gU2vxZ0Mvf96pctGnTJrz33nvQ6XTQ6XTYtGkTHnnkETzyyCPIzs4OyMN+SC1Xfs899+Do0aMoLi7GmTNnMH78eJSXl0OpVKKkpAQLFixAfn4+XC4Xdu/eLXW4RBFPI1chLzUHb9ftxndt52CIToJc1v1DgNPtQp2lAYM1euQNvQ6qXq46SjRQhcJy5Xv27IHT+e/WySeffBI5OTlYtGgRdDqd52H/vvvu6/PDfkglG4Ig4JlnnsEzzzzTad+cOXNw7NgxVFZWIjc3F0lJSRJESESXS9boccewqfjb6c/wreU81DIlBqm1Xi9Zs7rsaLQZ4RCdMEQnIy/1OiRpdNIFTUQew4YN8/ocGxuLxMREJCYmAkBAHvZDKtn4IampqUhNTZU6DCK6zJCoQZg34hYcNdXhcMtxnG5vglN0QUDHADaFIEdqVCImJozCFXEGn5c3Jxq4/H2/Sd/O7WkV0MsX7QrEw35YJRtEFLpiFVHI0l+BiboMfGc5D6OjDU7RBaUgR7wqFobowZDzDa9EXkKhG6U3/H3YZ7JBRAEll8mRFjtE6jCIwoK/gzwDMUC0P/Axg4iIiIKKLRtEREQSEeFnN0rAIgkutmwQERFRUDHZICIioqBiNwoREZFEImWAKJMNIiIiiYTL1Fd/sRuFiIiIgootG0RERBJhNwoREREFlwj/5q+yG4WIiIiILRtERESSiZCGDSYbREREkhGFjuLP+WGAyQYREZFEIqVlg2M2iIiIKKjYskFERCSRSFnUi8kGERGRZITviz/nhz52oxAREVFQsWWDiIhIIpHSjcKWDSIiIgoqJhtEREQUVOxGISIikox/L2ILlwGiTDaIiIikEiGrerEbhYiIiIKKyQYREREFFbtRiIiIJBIpU1+ZbBAREUmGK4gSERER+Y0tG0QUcmwuG6xuK1yiC0pBiSh5FBQy/ueKBqAImY3Cf71EFBJEUUSDrQEn2mpQ03oMNrcdouiGTJAjThGHMdqxGBmdhjhlnNShEgVMhOQaTDaISHomhwmfNe1FvaUOVrcVsfJYRMujIUCAW3SjxdGCTxr+gc8VBzAmbiyyEq6BUqaUOmwi6iUmG0QkqQv2C9h1fgfOWs9gkCoRg2SJEATvQW/Rimi4RTfMTjM+b65Eq7MVNyTdAKVMJVHURAESIU0bTDaISDJtzjbsbtiFc9azGKIZCrkg7/ZYmSBDvDIeGpkaR81fQyVT4frE/4BM4Dh3CmecjUJEFFRfm47gdPspJGtSekw0LqWWa6BX6vG16QjOWE8HOUIiCoSQSjZKS0shCEKnUlpaiqqqKmRnZyMhIQH5+fkQw2UlEyLqks1lwzetRxEtj4ZC8K2RNVoRDafowPHW40GKjqifiAEoYSCkko0FCxagubnZU+rq6pCYmIjrrrsOs2fPRlZWFiorK1FdXY3S0lKpwyUiP3xn+Q4t9mZolfF9Ol+r0OJk6wkYHcYAR0bUf6TINZqampCWloba2lrPtvfeew/p6elQKBSYPHkyjhw54tkXiIf9kEo2VCoVdDqdp5SVleGOO+7AkSNHYDQasXbtWmRkZGDVqlXYsGGD1OESkR/OWk8DAnxu1bgoVhEHi6sN563nAxwZ0cDV2NiIvLw8r0SjpqYGixcvRlFREU6dOoURI0bg/vvvBwDYbLaAPOyHVLJxKavVij/84Q8oKCjA4cOHkZOTg+joaABAZmYmqquruz3XZrPBZDJ5FSIKLRZXO+R+jFEXBAECBDhEewCjIupnAWrauPyeZ7PZuvy6efPmYd68eV7bjhw5glWrVuHuu+9GcnIyHn74YVRWVgIAtm3bFpCH/ZBNNjZt2oScnByMHDkSJpMJaWlpnn2CIEAul6O5ubnLcwsLCxEfH+8pBoOhv8Imol7qGEPvf4ezECaj8Ym6JgSgAAaDweu+V1hY2OW3lZSU4PHHH/falpeXh4ceesjz+ejRoxg1ahQA+Pyw352QTTbWr1/v+eMVCgXUarXXfo1GA4vF0uW5BQUFMBqNnlJXVxf0eInIN9HyaLhEV5/Pd4tuiBC5uBcRgLq6Oq/7XkFBQZfHpaen93gdu92O1atX45FHHgEAnx/2uxOS62wcP34cx48fx7Rp0wAAer0eVVVVXseYzWaoVF0v6KNWqzslJ0QUWoZEDUWV6Us43c4+vfek1dmKGEUsktUpQYiOKLxotVpotVq/r7NixQrExsbiwQcfBNDzw35CQkKvrxuSLRtvvvkm8vLyoFR2PLFkZ2ejoqLCs7+2thY2mw16vV6qEInIT4bo4UhQ6mF0tPTpfLPThIyYDL4rhcJbCE19/fjjj7F+/Xps2rTJc//V6/VoaGjwOq6nh/3uhGSy8eGHH+Kmm27yfJ46dSqMRiPKysoAAEVFRZg2bRrk8t4tAkREoUclU+GKuDFod7fD6Xb6dG6bsxVKmQrpsRlBio4ospw4cQILFy7ESy+9hHHjxnm2B+phP+SSjfb2duzbtw/XXXedZ5tCoUBJSQkeeughJCcn4+2330ZRUZGEURJRIIzV/giGqOE4azsDp9i7hKPd1Y4WRwuu1I5HimZIkCMkGvja29uRl5eHOXPm4Pbbb0draytaW1shimLAHvZDbsxGVFRUl1N25syZg2PHjqGyshK5ublISkqSIDoiCqQoeRRuGHwjdp3fiVPt9dApExAjj+n0IjYAcIkumBxGWFwWXKkdj2v1k/leFAp/IfAito8++ghHjhzBkSNH8PLLL3u2nzx5EiNHjkRJSQkWLFiA/Px8uFwu7N692+fvCLlkoyepqalITU2VOgwiCqB4pQ7TkqfjXxf24du2WrQ4mhElj4ZGpoYAGdxwoc3ZBqfohFYZj8m66zBRd3Wv36VCRJ1dugronDlzelwVNBAP+2GVbBDRwBSriMVNSTejWdeMk60ncKz1GKyudogQIRNkGKxJwZi4MRgRPRLRimipwyWKOP4+7DPZIKKQIAgC9Co99Ho9rtJNhN1th0t0QSlTQCVTs8uEBiZR6Cj+nB8GmGwQUchRyBR9WnuDiEITHxWIiIgoqPjoQEREJJUQmI3SH9iyQUREREHFlg0iIiKpsGWDiIiIyH8+JRtWq9VrzfSuTJ482a+AiIiIaGDpdbJx/vx5aDQa2Gw2OJ1OtLe3w263o729HU6nEzNmzAAAtLS0BCtWIiKigSWE3voaTL1KNtatW+d58ZlMJkN5eTliYmIQFRWF2NhYvP/++55X0EZHc3U/IiIi+rdeJRsJCQk4e/as17ZXX30VaWlp+M1vfgNRFBETExOUAImIiAYuIQAl9PUq2fjRj36Ec+fOeW0TBAGCIECtVgclMCIiooFOACCIfhSp/4Be6tXU15EjR6Kmpgbl5eVoa2vDvn374HA4YLFYUFVVhcbGRjQ1NaG8vBzt7e3BjpmIiIjCSK+SDZ1Oh6amJvzxj39ES0sLPvzwQ7jdbjQ3N2Pv3r2IiorC6dOn8cc//hEmkynYMRMREVEY6fWiXkOHDsWuXbswevRoPPPMM2hvb8fKlSvxwAMPICMjA3/4wx+wa9cuXH311cGMl4iIiMJMr6e+qlQqOJ1Or22CEC69RURERCSVXicb0dHRaGxs9HwuLi7G6dOn8dprrwEAx2oQERH5KkLW2eh1N0p8fDysVisEQUBOTg4KCgqgUqlgt9uRnZ0No9EIALDZbEELloiIiMJPr5ON999/HzKZDFarFcOHD8fw4cO99ldVVQHgCqJERES9xhexedNoNFAoFNiyZUunfcePH4dKpQIAHDhwIGDBERERUfjz6UVsTqcTjz/+uNe2xsZGXH/99di5cycAYMiQIYGLjoiIiMJer7tRgI4ZKXV1dZ7P58+fx/Tp0/GTn/wEN998c8CDIyIiGsgurgTqz/nhwKeWDaDjRWwmkwnPP/88MjMzsWDBArz44osYO3ZsMOIjIiKiMOdTywYA1NfXY8KECfjxj3+Mzz77DGlpaQDQaQ0OIiIiIqCXycagQYOgUqkgCAJcLhesVivee+89vP/++55jGhsbMXjwYDgcDjQ3NwctYCIiogEjQmaj9CrZ+OKLLzzJxuDBg5GUlIRZs2ZhyZIlGDRoEJxOJ7KysrB//37Y7fZgx0xERERhpFdjNlJTU5GUlITExEQYDAa8+OKLaGlpwTXXXINXX30V0dHRsNvtSEpKQmpqarBjJiIiojDi05gNt9sNALjhhhtwww03YOnSpbjzzjuxZ88efPLJJ0EJkIiIaMAShY7iz/lhoFctGydPnoTb7YbD4fCa4jpmzBjs3bsXJ0+exCuvvBK0IImIBiJRdEN0Hofb8i7crX+C2/wc3OYX4W77C0T7AYgi3zlFA0OvWjbuvvtunDlzBnfddRduvfVWbNq0yWv/3LlzoVar8frrrwMAFi5cGPhIiYgGCFG0A45DEO3/ApwnAdEGQAMIcgBuwGXr2CdPhqjMhqDKgiAfJHXYRH3Wq2Tj008/xWeffYaNGzfioYceQltbG6ZMmYJRo0ZBFP89FLa6uhpqtZrJBhFRN0R3K8T2dwD7AQByQDYYkMd0caADcDcC1vcgOg4C0XdDUKT1e7wUXJGyqFevkg2lUompU6di6tSpWLNmDV544QUUFxdj/vz5ePjhh4MdIxHRgCCK7RAtmwDHIUA+HBCiuz9YUALyIYA4GHCdhGjZCETfB0Exsr/CJQoYn1cQ1Wq1+PWvf40DBw5gxowZwYiJiGjAEUURYvt73ycaI3tONC4lyAF5BuBqhGh5A6L7QjDDJAoKn1cQvchgMAQyDiKigc1V19F1IhsCCBrfzhUEQJ4GuI5DtB2AEHVrcGKk/hchi3r53LJBRES+Ex2HANECCNq+XUCQA0Is4NgPUbQGNDaiYAvZZGP58uWYPXu253NVVRWys7ORkJCA/Px8r4GpREShTHSbAfvngKDraKXoK1kS4DoLOI4ELDaKPE1NTUhLS0Ntba1nW0/32EDcf0My2aiqqsK6detQXFwMALDZbJg9ezaysrJQWVmJ6upqlJaWShojEVGvOU8A7guALNG/6wgqAIDo/CYAQVFIEANQfNDY2Ii8vDyvRKOne2yg7r8hl2yIooglS5bgiSeeQEZGBgBg27ZtMBqNWLt2LTIyMrBq1Sps2LCh22vYbDaYTCavQkQkGdEKQPx+HQ1/KQE3/5s2kAh+lIsuv+fZbLYuv2vevHmYN2+e17ae7rG+3n+7E3LJxssvv4xDhw4hLS0NW7duhcPhwOHDh5GTk4Po6I7R25mZmaiuru72GoWFhYiPj/cUDmYlImk5A3cpQQaIfOEleTMYDF73vcLCwi6PKykpweOPP+61rad7rK/33+6EVLLR2tqKFStWYPTo0aivr8fatWsxdepUmEwmpKX9ezEbQRAgl8u7fZV9QUEBjEajp9TV1fXXn0BE1JmgDty1RBcg62IRMIpodXV1Xve9goKCLo9LT0/vtK2ne6yv99/uhFSyUV5ejra2NuzcuRNPP/00tm/fjpaWFrz66qtQq73/sWo0Glgsli6vo1arodVqvQoRkWRkOgBKwN93nYgiAHvHqqM0MARozMbl97zL75k9USgU3d5je9rni5BKNurr6zF58mTo9XoAHRWQmZkJq9WKhoYGr2PNZjNUKpUUYRIR+UaeBihGAK5z/l1HbAWEaAjK8YGJiwiAXq/v9h7b0z5fhFSyYTAY0N7unfl/++23WLNmDSoqKjzbamtrYbPZPEkJEVEoEwQ5BNW1AOwd3SB9JTYAitGAnOPQKHCys7O7vcf2tM8XIZVszJo1C0eOHMH69etRX1+P559/HocOHcL06dNhNBpRVlYGACgqKsK0adMglwdiZDcRUT9QjgfkgwH3qb6d7zYDAATVNRD8WauDQks/T33tytSpU7u9x/a0zxd9Xq48GPR6PT788EMsW7YMS5cuRUpKCjZv3oxRo0ahpKQECxYsQH5+PlwuF3bv3i11uEREvSbI4gBNHkTLXzsW5pKn9P5k0QK4TwPqqYByQvCCpIikUCi6vcf2tM8XghhGS3GeOnUKlZWVyM3NRVJSUq/PM5lMiI+Ph9Fo5GBRIpKMKIqAfW/HC9lENyBPBYQenvlEERCbAXcDoLoWQvQ9EHx9rwr5rD/uGRe/43cffIyomL7PLmpva8OvZt8akFh7usf29f57UUi1bPyQ1NRUpKamSh0GEVGfCIIAUTUFghAD0boVcJ0EoOiYXXLpdFbRAbgbAdHY8S4V9TQIUbMgCBwUP9AIYkfx5/xA6eke6+/9N6ySDSKicCcIAqC6umOgp+MriI5/Ac5awHkaHR3w368NKR8MKG+EoLoKkCVznAaFNSYbREQSEGSxgHoyoLoGcH3bsQS5aOvoVhGiAPlICLJoqcMkCggmG0REEhIEOaDovKojRQh/Z5SEyajLkJr6SkRERAMPkw0iIiIKKnajEBERSSVCulGYbBAREUnk+7lHfp0fDtiNQkREREHFlg0iIiKpREg3Cls2iIiIKKjYskFERCQVtmwQERER+Y/JBhEREQUVu1GIiIgkEkpvfQ0mtmwQERFRUDHZICIioqBiNwoREZFUOBuFiIiIyH9MNoiIiCiomGwQERFRUDHZICIioqDiAFEiIiKJCPBznY2ARRJcbNkgIiKioGLLBhERkVQ49ZWIiIjIf0w2iIiIKKjYjUJERCQVdqMQERER+Y8tG0RERBIR4N/01XCZ+spkg4iISCrsRiEiIiLyH5MNIiIiCip2oxAREUmF3ShERERE/mOyQUREFCE2btyI4cOHIzY2FtOmTUNtbS0AoKqqCtnZ2UhISEB+fj5EMbBNJiGXbDz66KMQBMFTRo0aBSD4FUFERNTfBNH/0ls1NTV46qmnsGXLFlRXV2PEiBFYtGgRbDYbZs+ejaysLFRWVqK6uhqlpaUB/TtDLtk4cOAA/va3v6G5uRnNzc04ePBgv1QEERHRQHbw4EHk5ORg0qRJGD58OBYvXoxvvvkG27Ztg9FoxNq1a5GRkYFVq1Zhw4YNAf3ukEo2nE4nqqqqMHXqVOh0Ouh0OsTFxfVLRRAREYUrk8nkVWw2W6djxo0bh507d+LgwYMwGo148cUXceutt+Lw4cPIyclBdHQ0ACAzMxPV1dUBjS+kko0vvvgCoihi4sSJiIqKwowZM/Ddd9/5XBE2m61TxRMREYUcUfS/ADAYDIiPj/eUwsLCTl81btw43HnnnZg0aRJ0Oh327duH1atXw2QyIS0tzXOcIAiQy+Vobm4O2J8ZUsnGkSNHcOWVV+Kvf/0rqquroVQqsWTJEp8rorCw0KvSDQZDf/0JRERE/a6urg5Go9FTCgoKOh1TUVGBDz74APv27YPZbMb8+fMxc+ZMKBQKqNVqr2M1Gg0sFkvA4gupZGPhwoWoqKhAdnY20tLS8MILL2D79u1wu90+VURBQYFXpdfV1fVH+ERERJLQarVe5fJ7JgC88cYbmDdvHq699lrExsZi5cqVOHHiBPR6PRoaGryONZvNUKlUAYsvpBf10ul0cLvdSElJQVVVlde+nipCrVZ3WdFEREShxNcZJV2d31tOp9OrR8BsNqOtrQ0KhQIVFRWe7bW1tbDZbNDr9X0P7DIh1bKxdOlSvPnmm57P+/fvh0wmw4QJE4JeEURERAPZlClTUF5ejueeew6bNm3CnDlzkJycjMceewxGoxFlZWUAgKKiIkybNg1yuTxg3x1SLRsTJ07EU089hZSUFDidTjz66KNYtGgRpk+f7qmI++67LygVQURENJDdc889OHr0KIqLi3HmzBmMHz8e5eXlUCqVKCkpwYIFC5Cfnw+Xy4Xdu3cH9LsFMcRWxyooKMD69esRFxeHuXPnYtWqVYiJicGWLVuwYMECxMXFeSriyiuv7NU1TSYT4uPjYTQaodVqg/wXEBFROOuPe8bF7yj+y3ZERcf0+TrtljY88dPpAYn11KlTqKysRG5uLpKSkvy61uVCLtnoSV8rgskGERH1VqQmG8EUUt0oPyQ1NRWpqalSh0FEREQ+CKtkg4iIaCDpz9koUgqp2ShEREQ08LBlg4iISCri98Wf88MAWzaIiIgoqJhsEBERUVCxG4WIiEgiHCBKREREFABMNoiIiCio2I1CREQkFc5GISIiIvIfWzaIiIikEiEtG0w2iIiIJCJ8X/w5PxywG4WIiIiCii0bREREUhHFjuLP+WGAyQZ1q81sxclvzsJisUGhlMMwMgmJyVoIQrg03BERUShgskGdOB0uVOz+GgcramC80ApAgCiKiI5V44orU3HjzKsQp42SOkwiovDHAaIUidxuN/6x7QtU/ONrxMRpMCwtCXK5DKIowmxsx8GKGpiN7bh94XWIidVIHS4REYUBDhAlL3UnG3Hgs+PQJ8UiMVkLubzjJyIIArS6aAwbmYjjR07jy8paaQMlIhoAhACUcMBkg7x8/cV3sFsdiIuP7nK/UqVAdIwaXx44Cbvd2c/RERENMGIAShhgskFe6msbER2r7vEYrS4aLRfaYGq29FNUREQUzphskO++b7cTw2TKFRERSYvJBnlJHZEIS5utx2NMLe3Q6qIRn9B1VwsREfUSu1EoEo3NNEClUsBsau9yv8PhRFurFZnXjIRKrezn6IiIKBwx2SAvw9OTcHXOKDSdN6GpwQy3yw2go8vEbGpH/clGpF8xBJnXpEscKRFR+IuU2ShcZ4O8yGQy3DQrE5ooJQ7tO4HvTjRAEASIEBEVrUbmNWm4OW8iYuK4xgYRkd+4XDlFKqVSgam3TcDVORk48c1ZWFptUKoUMKQlYvAQHZcrJyIinzDZoG7FxUfjqmx2lxARBVV4NE74hWM2iIiIKKjYskFERCSVCHkRG1s2iIiIKKjYskFERCQRf6evhstwfSYbREREUmE3ChEREZH/2LJBREQkmcho2mDLBhERkVQkfBHb8uXLMXv2bM/nqqoqZGdnIyEhAfn5+QF9s3dIJxszZsxAaWkpgOBWAhERUSSpqqrCunXrUFxcDACw2WyYPXs2srKyUFlZierqas/9NxBCNtl4/fXX8dFHHwEIfiUQERFJQRD9L74SRRFLlizBE088gYyMDADAtm3bYDQasXbtWmRkZGDVqlXYsGFDwP7OkEw2Lly4gGXLlmHMmDEAgl8JRERE4cxkMnkVm83W7bEvv/wyDh06hLS0NGzduhUOhwOHDx9GTk4OoqOjAQCZmZmorq4OWHwhmWwsW7YMc+fORU5ODgD4XAk2m61TxRMREQ1UBoMB8fHxnlJYWNjlca2trVixYgVGjx6N+vp6rF27FlOnToXJZEJaWprnOEEQIJfL0dzcHJD4Qi7Z2LVrF3bs2IHf/e53nm2+VkJhYaFXpRsMhqDHTURE5LOLr5j3pwCoq6uD0Wj0lIKCgi6/rry8HG1tbdi5cyeefvppbN++HS0tLXj11VehVqu9jtVoNLBYLAH5M0Mq2bBarViyZAleeuklaLVaz3aFQuFTJRQUFHhVel1dXVDjJiIikpJWq/Uql98zL6qvr8fkyZOh1+sBdNxfMzMzYbVa0dDQ4HWs2WyGSqUKSHwhlWw8++yzyM7OxqxZs7y26/V6nypBrVZ3qngiIqKQ089TXw0GA9rb2722ffvtt1izZg0qKio822pra2Gz2TxJib9CKtnYtGkT3nvvPeh0Ouh0OmzatAmPPPIIXnvttaBWAhERUSSYNWsWjhw5gvXr16O+vh7PP/88Dh06hOnTp8NoNKKsrAwAUFRUhGnTpkEulwfke0NqBdE9e/bA6XR6Pj/55JPIycnBokWLMG7cOJSVleG+++4LeCUQERFJoa/TVy893xd6vR4ffvghli1bhqVLlyIlJQWbN2/GqFGjUFJSggULFiA/Px8ulwu7d+/ue2CXCalkY9iwYV6fY2NjkZiYiMTExKBWAhERkTT6f7nynJwc7N27t9P2OXPm4NixY6isrERubi6SkpL8iMtbSCUbl7t04a5gVgIREREBqampSE1NDfh1QzrZuFywKoGIiEgSkfEettAaIEpEREQDD5MNIiIiCqqw6kYhIiIaUC5ZBbTP54cBJhtERERSiZAxG0w2iIiIJCJ8X/w5PxxwzAYREREFFVs2QpjT4UTtV3VoqGuCTCZg2JihGJqRAkEIl1yWiIh6xDEbJKXm80b8reRjfPtVHZwOFyACmhg1xuWOwfSf3QB1VNdv9CMiIgo1TDZCkMvpwrZX/o5jn5/EsFEpUEerIYoiWlssOPDxYUTFqjHtpzdIHSYREVGvcMxGCPq2uh4nv/wOqd8nGgAgCALiEmKgS4rHl3u+hqnJLHGURETkt35+xbxUmGyEoIb6JjgdLmiiO3eVxCdpYb7Qiob6JgkiIyKigLo4ZsOfEgaYbIQgmVwGiIDYxY/I7XJDJhc6jiEiIgoDvGOFIMOYoYiK08B8obXTvqZTFzBoqB5D0pMliIyIiMh3TDZCUPKIJEz4jx+h8XQzmk43w+V0wW5z4MzJ83A4nJg8c1KXXSxERBRmIqQbhbNRQpAgCLjlp1OhidXgi93VqD92BjKZgEFD9bh25iRcffN4qUMkIiLqNSYbIUqlVuKme6Yg+7aJaKhrhEwuQ0raYK6vQUREYYfJRoiL1cUgVhcjdRhERBQMfBEbERERBZMgihD8GHfhz7n9iQNEiYiIKKiYbBAREVFQsRuFiIhIKhHy1le2bBAREVFQsWWDiIhISuHROOEXJhtERESSiYy5r+xGISIioqBiywYREZFUIqNhg8kGERGRZDgbhYiIiMh/bNkgIiKSTGT0ozDZICIikkpk5BpMNoiIiCQjws8xGwGLJKg4ZoOIiIiCiskGERERBRW7UYiIiKTCqa9EREQ0UM2YMQOlpaUAgKqqKmRnZyMhIQH5+fkQA5zEhGSy0dTUhE8//RSNjY1Sh0JERDTgvP766/joo48AADabDbNnz0ZWVhYqKytRXV3tSUICJeSSjc2bN2PUqFH4xS9+geHDh2Pz5s0Agp91ERER9buL3Sj+FAAmk8mr2Gy2br/ywoULWLZsGcaMGQMA2LZtG4xGI9auXYuMjAysWrUKGzZsCOifGVLJRktLCx599FHs2bMHBw8exJ/+9Cf86le/6pesi4iIKFwZDAbEx8d7SmFhYbfHLlu2DHPnzkVOTg4A4PDhw8jJyUF0dDQAIDMzE9XV1QGNL6QGiJrNZhQXF2P8+PEAgKuuugrNzc1eWVd0dDRWrVqFX/ziF1i8eHGX17HZbF5Znclk6pf4w40oimg6Z0KbqR1ypRzJqQlQqkLqJ0FENLAFaIBoXV0dtFqtZ7Nare7y8F27dmHHjh2oqqrCY489BqDjHpmWluY5RhAEyOVyNDc3IyEhoe+xXSKk7iwGgwELFy4EADgcDqxevRp33HGHz1lXYWEh/vu//7tfYg5X9ScbsP8fR3Dy6zOwttshl8uQOESHideNwsTrRkGukEsdIhER9ZJWq/VKNrpitVqxZMkSvPTSS17HKhSKTsmJRqOBxWIJWLIRUt0oFx0+fBjJycnYvn07iouLe8y6ulJQUACj0egpdXV1/RV6WKj95ize/fMnqNp/ElExaqSOSERicjyaz5vw4Zv/ws73DsLlcksdJhERBdCzzz6L7OxszJo1y2u7Xq9HQ0OD1zaz2QyVShWw7w6plo2LMjMzsWPHDjz55JNYvHgxrrjiCp+yLrVa3W0TUqSz25zYueUAWlvaMfKKFAiCAACQK+RIMQyC2WjBgT1HMXJMCkaPHyZxtEREA1w/rrOxadMmNDQ0QKfTAQAsFgvefPNNjBw5Eg6Hw3NcbW0tbDYb9Hp93+O6TEi2bAiCgKuvvhqlpaV47733+iXrihS135zB2fpmpBj0nkTjUnHx0XC73Piq8qQE0RERRRZRFP0uvbVnzx5UVVXh0KFDOHToEH7yk5/gt7/9LT755BMYjUaUlZUBAIqKijBt2jTI5YHrTg+plo2dO3di27Zt+P3vfw+gox8JAMaOHYtXXnnFc1wwsq5I0XjWCLfL1eNA0Nj4KNSfbIDL6eLYDSKiYOrHt74OG+bdWh0bG4vExEQkJiaipKQECxYsQH5+PlwuF3bv3u1HUJ2FVLIxduxYzJkzB6NHj8aPf/xjrFixAtOnT8esWbPwwAMPoKysDPfdd19Qsi4iIqJIcukSEnPmzMGxY8dQWVmJ3NxcJCUlBfS7QqobZejQoXjrrbdQXFyMK6+8EhaLBRs3boRCoUBJSQkeeughJCcn4+2330ZRUZHU4YalxJR4yGQyOOzObo8xt1gwLC2JrRpEREEnBqAERmpqKm6//faAJxpAiLVsAMBtt93W5bTWYGddkWLkFUOQYtDjbN0FDEtP6jRuw2y0QK6Q48pr0rq5AhERBUyEvIgt5JKNnqSmpiI1NVXqMMKaSq3AzXOy8MFf9qL2m7NITIlHdIwaTocLzY1m2GxOXHvjj5D+o6FSh0pERANEWCUbFBgjr0jB3MVTPYt6NTeaOy/qJQ+pHjYiooGpHweISonJRoQalpaE1JGJXK6ciEhK7EahgU4QBCSmxCMxJV7qUIiIaABjskFERCSZyOhHYbJBREQklcjINUJrnQ0iIiIaeNiyQUREJBUOECUiIqJg8vVlal2dHw6YbBAREUkmMgZtcMwGERERBRVbNoiIiKQSGQ0bTDaIiIik4+cA0TDJNiIi2bg4gMZkMkkcCRERhbqL94r+GHzZbmuX9Pz+EhHJRlNTEwDAYDBIHAkREYWLpqYmxMcH53UOKpUKKSkp+P+fe8Tva6WkpEClUgUgquARxHCZN+OHlpYWJCQk4LvvvgvaD2cgM5lMMBgMqKurg1arlTqcsMK66zvWnX9Yf31nNBoxfPhwNDc3Q6fTBe17rFYr7Ha739dRqVTQaDQBiCh4IqJlQybrmHQTHx/Pf3R+0Gq1rL8+Yt31HevOP6y/vrt47wgWjUYT8klCoHDqKxEREQUVkw0iIiIKqohINtRqNZ555hmo1WqpQwlLrL++Y931HevOP6y/vmPdBV5EDBAlIiIi6UREywYRERFJh8kGERERBRWTDSIiIgoqJhtEREQUVAM+2aiqqkJ2djYSEhKQn5/fL2vdh7NHH30UgiB4yqhRowCwHnvS1NSEtLQ01NbWerb1VF+sy3/rqu66+w0CrLtLvffee0hPT4dCocDkyZNx5MgRAPzt9UZ3dcffXvAM6GTDZrNh9uzZyMrKQmVlJaqrq1FaWip1WCHtwIED+Nvf/obm5mY0Nzfj4MGDrMceNDY2Ii8vz+tm2VN9sS7/rau6A7r+DQKsu0vV1NRg8eLFKCoqwqlTpzBixAjcf//9/O31Qnd1B/C3F1TiAPbuu++KCQkJYltbmyiKonjo0CFxypQpEkcVuhwOhxgXFyeazWav7azH7t1yyy1icXGxCEA8efKkKIo91xfr8t+6qrvufoOiyLq71AcffCC+9NJLns87d+4UVSoVf3u90F3d8bcXXAO6ZePw4cPIyclBdHQ0ACAzMxPV1dUSRxW6vvjiC4iiiIkTJyIqKgozZszAd999x3rsQUlJCR5//HGvbT3VF+vy37qqu+5+gwDr7lJ5eXl46KGHPJ+PHj2KUaNG8bfXC93VHX97wTWgkw2TyYS0tDTPZ0EQIJfL0dzcLGFUoevIkSO48sor8de//hXV1dVQKpVYsmQJ67EH6enpnbb1VF+sy3/rqu66+w0C/PfcHbvdjtWrV+ORRx7hb89Hl9Ydf3vBNaDf+qpQKDotN6vRaGCxWJCQkCBRVKFr4cKFWLhwoefzCy+8gPT0dIwdO5b16IOefnf8Tfasu9+gyWRi3XVjxYoViI2NxYMPPogVK1bwt+eDS+tOqVTytxdEA7plQ6/Xo6GhwWub2WyGSqWSKKLwotPp4Ha7kZKSwnr0QU+/O/4mfXPxN3jmzBnWXRc+/vhjrF+/Hps2bYJSqeRvzweX193l+NsLrAGdbGRnZ6OiosLzuba2FjabDXq9XsKoQtfSpUvx5ptvej7v378fMpkMEyZMYD36oKffHX+TPevuN2gwGFh3lzlx4gQWLlyIl156CePGjQPA315vdVV3/O0FmcQDVIPK4XCISUlJ4muvvSaKoiguWbJEzMvLkziq0PXaa6+Jo0aNEnfv3i3u2LFDHDt2rPjzn/+c9dgLuGxGRXf1xbrs7NK66+43KIqsu0tZLBbxRz/6kfjAAw+IZrPZU+x2O397P6C7uistLeVvL4gGdLIhih1TlqKiosTBgweLgwYNEquqqqQOKaQtX75c1Ol0osFgEB977DGxtbVVFEXW4w+59IYpij3XF+vS2+V1191vUBRZdxe9++67IoBO5eTJk/zt/YCe6o6/veCJiFfMnzp1CpWVlcjNzUVSUpLU4YQt1qNveqov1mXfse5+GH97wcG667uISDaIiIhIOgN6gCgRERFJj8kGERERBRWTDSIiIgoqJhtEREQUVEw2iAa4r7/+Gu+++y6cTmevz3G73aiurobJZApiZEQUKZhsEA1wb731FtasWQOFovevQnK73Zg6dSreeeedIEZGRJGCU1+JBqgbbrgBNTU10Ol0XttFUUR1dTVaW1sRExODr776Cv/xH/+BhIQECILgOc5oNEKpVHpeqw0ADocDWq0WX375ZX/9GUQ0ADDZIBqgbrvtNsyfPx+LFi3y2m61WhEVFQWHw+HV2uFyufDXv/612+uNGTMG2dnZwQqXiAYwJhtEA1ReXh4qKioQGxvrtV0URXz33Xe4/J++0+mEUqlEWVlZp7dgbtu2DXq9Hs8991zQ4yaigaf3nbhEFHZWr17dbcsGAGzfvh3Tp08H0NGyAQBFRUVe3SkA0NTUhLvuuiv4ARPRgMSWDaIB6odaNpYvX47XX38d27dvx9ixYyWKkogiAVs2iAawrlo2zp49iyFDhqCiogIHDx7EoEGDMGXKFBw+fPgHZ6zYbDYsXrwY69atC2LURDTQsGWDaIAZN24cTp8+DYvFArVaDbVa7bXfYrGgvb0ddrvdMzbj5ptvxtKlS5GXl9fjtVeuXIkLFy5g7dq1QYufiAYetmwQDTC7d++GXC6HTPbvZXTa2trwm9/8Blu3bsU777yDmTNneg0CvfTYCRMmQBAEqFQqAMCFCxcwefJkz0yVS48lIuoN/leDaIBJSkqCXq+HTqdDVFQU3n77beTm5qKlpQWHDh3CTTfd1OmcSxs4tVot3n//fVRWVqKyshK//e1vO81OISLyBVs2iAYYm82GTz/9FOXl5Xjrrbdw4403ory8HFlZWQAAs9nc6Ry32+35/waDAXfeeafX/oszVtxuN1s2iMhnTDaIBhCHw4Ef//jHOHHiBH72s59h//79MBgMnv0PPvggtmzZgtGjR3udZ7fbPf9/8+bNXV579erVeP311zleg4h8xgGiRAOM2WxGbGxsp7UyAODQoUOoqanBjTfeiEGDBvl03YsDSru6LhFRT5hsEBERUVCx85WIiIiCiskGERERBRWTDSIiIgoqJhtEREQUVEw2iIiIKKiYbBAREVFQMdkgIiKioGKyQUREREH1/wDXzvojUjA5/QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 2. 直方图\n",
    "- \t直方图是一种用于展示数据分布的图表。它将数据分成若干个区间（称为“箱子”或“bin”），并统计每个区间内数据点的数量。直方图通常用于展示数据的频率分布。\n",
    "- 函数\n",
    "    plt.hist(x, bins=None, range=None, density=False, weights=None,\n",
    "         cumulative=False, bottom=None, histtype='bar', align='mid',\n",
    "         orientation='vertical', rwidth=None, log=False, color=None,\n",
    "         label=None, stacked=False, **kwargs)\n"
   ],
   "id": "bcd0739b5bbc2b87"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T06:20:38.199136Z",
     "start_time": "2025-09-08T06:20:38.122122Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = pd.read_excel(path + '梁山.xlsx')\n",
    "# 绘制直方图\n",
    "plt.hist(x=data.成绩,bins=10)\n",
    "\n",
    "\n",
    "plt.show()"
   ],
   "id": "fe3675e8ea20899f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
